System and method for enhanced chatflow application

ABSTRACT

Embodiments provide a computer implemented method of training an enhanced chatflow system, comprising: ingesting a corpus of information comprising at least one user input node corresponding to a user question and at least one expert-designed variation for each user input node; matching one or more user inputs to one or more corresponding dialog nodes using regular expressions and delimiters; ingesting one or more usage logs from a deployed dialog system, each usage log comprising at least one user input node; for each user input node: designating the node as a class; storing the node in a dialog node repository; designating each of the at least one variations as training examples for the designated class; converting the classes and the training examples into feature vector representations; training one or more classifiers and one or more classification objectives using the feature vector representations.

TECHNICAL FIELD

The present application relates generally to a system and method thatcan be used to create a more efficient and accurate classifier systemfor the use in chatflow applications.

BACKGROUND

Existing chatflow dialog systems use a human expert-designed, rule-basedapproach to the development of dialog applications. Such an approach hasvery high accuracy but relatively low recall. It requires thedevelopment of a large number of variations of possible user input by ahuman expert in order to achieve such accuracy. It also requires theintroduction of new nodes and variations when the system fails on unseenuser input. A “node” is an answer or system block called upon by thepresence of a particular user input, while a “variation” is a semanticalreordering of a particular user input that conveys the same request asthe original user input, but uses a different grammatical structure ordifferent terms. What is needed is to strike a better balance betweenaccuracy and recall by using a statistical classifier approach inparallel with a rule-based system.

SUMMARY

Embodiments can provide a computer implemented method, in a dataprocessing system comprising a processor and a memory comprisinginstructions which are executed by the processor to cause the processorto train an enhanced chatflow system, the method comprising: ingesting,using a rule-based module, a corpus of information comprising at leastone user input node corresponding to a user question and at least oneexpert-designed variation for each user input node; matching, using therule-based module, one or more user inputs to one or more correspondingdialog nodes using regular expressions and delimiters; ingesting, usinga statistical matching module, one or more usage logs from a deployeddialog system, each usage log comprising at least one user input node;for each user input node: designating the node as a class; storing thenode in a dialog node repository; designating each of the at least onevariations as training examples for the designated class; converting theclasses and the training examples into feature vector representations;training one or more classifiers using the one or more feature vectorrepresentations of the classes; training classification objectives usingthe one or more feature vector representations of the training examples;and incorporating the training of the classifiers and the classificationobjectives into enhanced chatflow system.

Embodiments can further provide a method further comprising ingesting,using the statistical matching module, the expert-designed variations inaddition to the usage logs.

Embodiments can further provide a method further comprising expanding,using the rule-based module, the regular expressions and delimitersusing synonyms harvested through one or more external data sources.

Embodiments can further provide a method further comprising fixing therule-based module such that no further alteration to the regularexpressions or delimiters is allowed; ingesting, using the statisticalmatching module, the expert-designed variations in addition to the usagelogs; and training, using the statistical matching module, new classesbased upon analysis of the expert-designed variations.

Embodiments can further provide a method further comprising ingesting,using the statistical matching module, the expert-designed variations inaddition to the usage logs; training, using the statistical matchingmodule, new classes based upon analysis of the expert-designedvariations; and replacing the rule-based module with the statisticalmatching module.

Embodiments can further provide a method further comprising training,using the statistical matching module, the classifiers and theclassification objectives using at least one of: linear regression,logistic regression, Multi-Layer-Perceptrons (MLP), and Deep BeliefNetwork (DBN) classifiers.

Embodiments can further provide a method further comprising converting,using the statistical matching module, the classes and training examplesinto feature vector representations using at least one of: directlycombining word vectors, convolutional neural networks to transformvariable length sentences to fixed-length feature vectorrepresentations, or sentence-to-vector encoders trained on general ordomain specific corpora.

In another illustrative embodiment, a computer program productcomprising a computer usable or readable medium having a computerreadable program is provided. The computer readable program, whenexecuted on a processor, causes the processor to perform various onesof, and combinations of, the operations outlined above with regard tothe method illustrative embodiment.

In yet another illustrative embodiment, a system is provided. The systemmay comprise an enhanced chatflow processor configured to performvarious ones of, and combinations of, the operations outlined above withregard to the method illustrative embodiment.

Additional features and advantages of this disclosure will be madeapparent from the following detailed description of illustrativeembodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present invention are bestunderstood from the following detailed description when read inconnection with the accompanying drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentsthat are presently preferred, it being understood, however, that theinvention is not limited to the specific instrumentalities disclosed.Included in the drawings are the following Figures:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system implementing an enhanced chatflow system in a computernetwork;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 depicts a flowchart illustrating a training mode for an enhancedchatflow system, in accordance with embodiments described herein;

FIG. 4 depicts a flowchart illustrating a statistical matching modulefunctionality during the deployment of an enhanced chatflow system,according to embodiments described herein;

FIG. 5 depicts a flowchart illustrating hybrid rule-based andstatistical matching functionality during the deployment of an enhancedchatflow system, according to embodiments described herein; and

FIG. 6 depicts a flowchart illustrating hybrid rule-based andstatistical matching functionality during the deployment of an enhancedchatflow system, according to embodiments described herein.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a head disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network(LAN), a wide area network (WAN) and/or a wireless network. The networkmay comprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computers,and/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including anobject-oriented programming language such as Java, Smalltalk, C++ or thelike, and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computer,or entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including LAN or WAN, or the connection may be made toan external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operations steps to be performed on the computer,other programmable apparatus, or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical functions. In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. IBMWatson™ is an example of one such cognitive system which can processhuman readable language and identify inferences between text passageswith human-like accuracy at speeds far faster than human beings and on amuch larger scale. In general, such cognitive systems are able toperform the following functions:

-   -   Navigate the complexities of human language and understanding    -   Ingest and process vast amounts of structured and unstructured        data    -   Generate and evaluate hypotheses    -   Weigh and evaluate responses that are based only on relevant        evidence    -   Provide situation-specific advice, insights, and guidance    -   Improve knowledge and learn with each iteration and interaction        through machine learning processes    -   Enable decision making at the point of impact (contextual        guidance)    -   Scale in proportion to the task    -   Extend and magnify human expertise and cognition    -   Identify resonating, human-like attributes and traits from        natural language    -   Deduce various language specific or agnostic attributes from        natural language    -   High degree of relevant recollection from data points (images,        text, voice) (memorization and recall)    -   Predict and sense with situation awareness that mimic human        cognition based on experiences    -   Answer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for answeringquestions posed to these cognitive systems using a Question Answeringpipeline or system (QA system). The QA pipeline or system is anartificial intelligence application executing on data processinghardware that answers questions pertaining to a given subject-matterdomain presented in natural language. The QA pipeline receives inputsfrom various sources including input over a network, a corpus ofelectronic documents or other data, data from a content creator,information from one or more content users, and other such inputs fromother possible sources of input. Data storage devices store the corpusof data. A content creator creates content in a document for use as partof a corpus of data with the QA pipeline. The document may include anyfile, text, article, or source of data for use in the QA system. Forexample, a QA pipeline accesses a body of knowledge about the domain, orsubject matter area (e.g., financial domain, medical domain, legaldomain, etc.) where the body of knowledge (knowledgebase) can beorganized in a variety of configurations, e.g., a structured repositoryof domain-specific information, such as ontologies, or unstructured datarelated to the domain, or a collection of natural language documentsabout the domain.

Using data derived from an already completed/deployed Watson EngagementAdvisor (WEA)-type dialog application, a corpus of example and variationsentences coded by a human designer can be classified as the input tothe classification training, while the nodes that they belong to can beclassified as the classification objective. Alternatively, theclassification objective can be directly classified as the expectedresponse to the given input or the action that needs to be taken that istriggered by the given input. The example and variation sentences can betransformed into feature vector representations using any availabletechnique, including by not limited to: directly combining word vectors,using convolutional neural networks to transform variable lengthsentences to fixed-length feature vector representations, or usingsentence-to-vector encoders trained on general or domain specificcorpora. These transformations can be performed in order to takeadvantage of the principle that feature vector representations orembedding capture the semantic meaning of words and phrases so as toallow for generalization on unseen examples, variations, or synonyms.Classifiers can then be trained based on different algorithmicapproaches, including, but not limited to: linear regression, logisticregression, Multi-Layer-Perceptrons (MLP), and Deep Belief Network (DBN)classifiers using any methodology for word and sentence embedding.

The user input can be transformed into a vector representation using thesame method used during the training the classifier. The user inputprocessed by the trained classifier can give the probability that thisinput belongs to one of the corresponding classes/WEA dialog nodes, ordirect triggers a specific response or action (depending on how theclassifier has been trained). This probability can be used as aconfidence score and the system can either pick the class with thehighest probability as the desired output node or can use the confidencescore for a fusion methodology.

Introducing statistical matching along with the existing rule-baseddialog system can lead to a faster initial deployment of a new dialogapplication and to a continuously improving system once deployed. Inparticular, the use of statistical matching can reduce the time neededto introduce all the necessary input variations to achieve a level ofperformance. The deployed enhanced chatflow system can be used tocontinuously improve the statistical matching in the case of missingparaphrases. Thus, the system can be quickly updated and performance canbe improved based on new user input. Various embodiments fordevelopingand updating the rule-based matching system along with the training andre-training of the statistical-based matching system can be employed,including:

Expert-designed variations can be used both for the rule-base and thestatistical-based matching. Further expansion of the regular expressionsand delimiters with synonyms, example sentences, etc. can be either handcrafted or harvested through external data sources (such as theinternet) in order to increase the amount of data to train thestatistical matching.

Expert-designed variations can be used solely for the rule-basedmatching. Usage logs can be used to extend the rule-based system's nodesand variations. Statistical matching can use both the variations and theusage logs to continuously train.

Expert-designed variations can be used solely for the rule-basedmatching, which can be subsequently fixed once deployed. Statisticalmatching can use both the variations and the usage logs to continuouslytrain, and can include the introduction of new classes.

Expert-designed variations can be used solely for the rule-basedmatching, which can be fixed once deployed. Statistical matching can betrained only on the usage logs to catch cases where the rule-basedmatching fails.

Expert-designed variations can be used for the rule-based matching forthe initially deployed system. Statistical matching can be trained onvariations and usage logs and can gradually replace the rule-basedsystem.

The statistical classifier can be automatically and continuously trainedon new data collected after the deployment of the initial system. Byusing a classifier system trained on a (relatively small) number ofinitial human-designed variations, the enhanced chatflow system can: a)be able to achieve a good level of performance on Watson EngagementAdvisor (WEA) dialog applications without the need for a comprehensivedevelopment of all possible variations and concepts—thus decreasingapplication development time; and b) increase the accuracy of chatflowmatching in completed or deployed applications by using a classifiercontinuously trained on example data collected from users chatting withthe system. Thus, the enhanced chatflow system can have an effect notonly on the quality of the deployed dialog solution but also on thespeed/practice of development of a new dialog solution.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system 100 implementing a question and answer (QA) pipeline108 and an enhanced chatflow system 120 in a computer network 102. Oneexample of a question/answer generation operation which may be used inconjunction with the principles described herein is described in U.S.Patent Application Publication No. 2011/0125734, which is hereinincorporated by reference in its entirety. The cognitive system 100 isimplemented on one or more computing devices 104 (comprising one or moreprocessors and one or more memories, and potentially any other computingdevice elements generally known in the art including buses, storagedevices, communication interfaces, and the like) connected to thecomputer network 102. The network 102 includes multiple computingdevices 104 in communication with each other and with other devices orcomponents via one or more wired and/or wireless data communicationlinks, where each communication link comprises one or more of wires,routers, switches, transmitters, receivers, or the like. The cognitivesystem 100 and network 102 enables enhanced chatflow functionality forone or more cognitive system users via their respective computingdevices. Other embodiments of the cognitive system 100 may be used withcomponents, systems, sub-systems, and/or devices other than those thatare depicted herein.

The cognitive system 100 is configured to implement a QA pipeline 108that receive inputs from various sources. For example, the cognitivesystem 100 receives input from the network 102, a corpus of electronicdocuments 140, cognitive system users, and/or other data and otherpossible sources of input. In one embodiment, some or all of the inputsto the cognitive system 100 are routed through the network 102. Thevarious computing devices 104 on the network 102 include access pointsfor content creators and QA system users. Some of the computing devices104 include devices for a database storing the corpus of data 140.Portions of the corpus of data 140 may also be provided on one or moreother network attached storage devices, in one or more databases, orother computing devices not explicitly shown in FIG. 1. The network 102includes local network connections and remote connections in variousembodiments, such that the cognitive system 100 may operate inenvironments of any size, including local and global, e.g., theInternet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 140 for use as part of a corpus of data with thecognitive system 100. The document includes any file, text, article, orsource of data for use in the cognitive system 100. QA system usersaccess the cognitive system 100 via a network connection or an Internetconnection to the network 102, and input questions to the cognitivesystem 100 that are answered by the content in the corpus of data 140.In an embodiment, full questions can be generated and entered into theQA system using the enhanced chatflow system 120 described herein. Thecognitive system 100 parses and interprets a full question via a QApipeline 108, and provides a response containing one or more answers tothe question. In some embodiments, the cognitive system 100 provides aresponse to users in a ranked list of candidate answers while in otherillustrative embodiments, the cognitive system 100 provides a singlefinal answer or a combination of a final answer and ranked listing ofother candidate answers.

The cognitive system 100 implements the QA pipeline 108 which comprisesa plurality of stages for processing an input question and the corpus ofdata 140. The QA pipeline 108 generates answers for the input questionbased on the processing of the input question and the corpus of data140. In some illustrative embodiments, the cognitive system 100 may bethe IBM Watson™ cognitive system available from International BusinessMachines Corporation of Armonk, N.Y., which is augmented with themechanisms of the illustrative embodiments described hereafter. Asoutlined previously, a QA pipeline of the IBM Watson™ cognitive systemreceives an input question, which it then parses to extract the majorfeatures of the question, and which in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question. TheQA pipeline of the IBM Watson™ cognitive system then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. The scoresobtained from the various reasoning algorithms are then weighted againsta statistical model that summarizes a level of confidence that the QApipeline of the IBM Watson™ cognitive system has regarding the evidencethat the potential response, i.e., candidate answer, is inferred by thequestion. This process is repeated for each of the candidate answers togenerate a ranked listing of candidate answers which may then bepresented to the user that submitted the input question, or from which afinal answer is selected and presented to the user. More informationabout the QA pipeline of the IBM Watson™ cognitive system may beobtained, for example, from the IBM Corporation website, IBM Redbooks,and the like. For example, information about the QA pipeline of the IBMWatson™ cognitive system can be found in Yuan et al., “Watson andHealthcare,” IBM developerWorks, 2011 and “The Era of Cognitive Systems:An Inside Look at IBM Watson and How it Works” by Rob High, IBMRedbooks, 2012.

As shown in FIG. 1, in accordance with some illustrative embodiments,the cognitive system 100 is further augmented, in accordance with themechanisms of the illustrative embodiments, to include logic implementedin specialized hardware, software executed on hardware, or anycombination of specialized hardware and software executed on hardware,for implementing an enhanced chatflow system 120. As described furtherin FIGS. 3-6, the enhanced chatflow system 120 can receive user input150, which can take the form of one or more user-generated question thatcan warrant an informational response. For instance, “How do I find carparking?” In an embodiment, the user input can be generated by userinputs previously collected during input into a general chatflowapplication. The enhanced chatflow system 120 can utilize the user data150 using a sentence vector conversion module 122, a classifier trainermodule 123, a dialog node repository 124, and a statistical matchingmodule 125, to train classifiers 127 for statistical matching, which inturn can be used, either in combination with a standard rule-basedmodule 126, or alone, to further refine and enhance the chatflow system,leading to improved deployment times and continuous refinement andtraining.

FIG. 2 is a block diagram of an example data processing system 200 inwhich aspects of the illustrative embodiments are implemented. Dataprocessing system 200 is an example of a computer, such as a server orclient, in which computer usable code or instructions implementing theprocess for illustrative embodiments of the present invention arelocated. In one embodiment, FIG. 2 represents a server computing device,such as a server, which implements the enhanced chatflow system 120 andcognitive system 100 described herein.

In the depicted example, data processing system 200 can employ a hubarchitecture including a north bridge and memory controller hub (NB/MCH)201 and south bridge and input/output (I/O) controller hub (SB/ICH) 202.Processing unit 203, main memory 204, and graphics processor 205 can beconnected to the NB/MCH 201. Graphics processor 205 can be connected tothe NB/MCH through an accelerated graphics port (AGP).

In the depicted example, the network adapter 206 connects to the SB/ICH202. The audio adapter 207, keyboard and mouse adapter 208, modem 209,read only memory (ROM) 210, hard disk drive (HDD) 211, optical drive (CDor DVD) 212, universal serial bus (USB) ports and other communicationports 213, and the PCI/PCIe devices 214 can connect to the SB/ICH 202through bus system 216. PCI/PCIe devices 214 may include Ethernetadapters, add-in cards, and PC cards for notebook computers. ROM 210 maybe, for example, a flash basic input/output system (BIOS). The HDD 211and optical drive 212 can use an integrated drive electronics (IDE) orserial advanced technology attachment (SATA) interface. The super I/O(SIO) device 215 can be connected to the SB/ICH.

An operating system can run on processing unit 203. The operating systemcan coordinate and provide control of various components within the dataprocessing system 200. As a client, the operating system can be acommercially available operating system. An object-oriented programmingsystem, such as the Java™ programming system, may run in conjunctionwith the operating system and provide calls to the operating system fromthe object-oriented programs or applications executing on the dataprocessing system 200. As a server, the data processing system 200 canbe an IBM® eServer™ System p® running the Advanced Interactive Executiveoperating system or the Linux operating system. The data processingsystem 200 can be a symmetric multiprocessor (SMP) system that caninclude a plurality of processors in the processing unit 203.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as the HDD 211, and are loaded into the main memory 204 forexecution by the processing unit 203. The processes for embodiments ofthe enhanced chatflow system can be performed by the processing unit 203using computer usable program code, which can be located in a memorysuch as, for example, main memory 204, ROM 210, or in one or moreperipheral devices.

A bus system 216 can be comprised of one or more busses. The bus system216 can be implemented using any type of communication fabric orarchitecture that can provide for a transfer of data between differentcomponents or devices attached to the fabric or architecture. Acommunication unit such as the modem 209 or network adapter 206 caninclude one or more devices that can be used to transmit and receivedata.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIG. 2 may vary depending on the implementation. Otherinternal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives may be used inaddition to or in place of the hardware depicted. Moreover, the dataprocessing system 200 can take the form of any of a number of differentdata processing systems, including but not limited to, client computingdevices, server computing devices, tablet computers, laptop computers,telephone or other communication devices, personal digital assistants,and the like. Essentially, data processing system 200 can be any knownor later developed data processing system without architecturallimitation.

FIG. 3 depicts a flowchart illustrating a training mode for an enhancedchatflow system, in accordance with embodiments described herein. Todefine: a “node” can be an answer or answer chain called upon by thepresence of an input question, while a “variation” is a semanticalreordering of a particular question that conveys the same request as theoriginal question, but uses a different grammatical structure or wordchoice. Both the normal questions and the variations can be consideredas user input 150 (as shown in FIG. 1). Upon reception of a corpus ofuser input 300 previously derived from an already completed/deployeddialog system 305, the system can designate each user input node (whichcan be an example or exemplar question pointing to a response) as aclass 301, and store them in a dialog node repository 124 (as shown inFIG. 1). A completed/deployed dialog system can be a system that hasalready been in use for a predetermined period of time, such that one ormore users can have interacted with the system and input questions andreceived answers. In an embodiment, the completed dialog system 305 canbe a standard rule-based module 126 (as shown in FIG. 1), which can usea call-and-response model to analyze an input question in order tooutput a proscribed answer. The question and answer pairings can bepreviously input into the completed dialog system 305 by a subjectmatter expert with knowledge of the particular field or activity thecompleted dialog system 305 is designed. For each node, the system candesignate all possible variations of the node as training examples forthe class 302.

The enhanced chatflow system can then convert the user nodes and theirtraining examples (variations) into feature vector representations 303.The system can, through a sentence vector conversion module 122 (asshown in FIG. 1), utilize a multitude of techniques to perform thevector representation conversion, including, but not limited to:directly combining word vectors, using convolutional neural networks totransform variable length sentences to fixed-length feature vectorrepresentations, or using sentence-to-vector encoders trained on generalor domain specific corpora. After conversion, the system, using aclassifier trainer module 123 (as shown in FIG. 1) can then train one ormore classifiers using the one or more converted examples and theirvariations, as well as train one or more classification objectives asthe particular node to which the example and its variations correspond304. The classifiers and the classification objectives can be trainedbased on a multitude of different algorithmic approaches, including butnot limited to: linear regression, logistic regression,Multi-Layer-Perceptrons (MLP), and Deep Belief Network (DBN) classifiersusing any methodology for word and sentence embedding. The enhancedchatflow system can then use the classifiers and classificationobjectives derived from the completed dialog system 350 in order toenhance analysis of the user input generated in the new dialog system.

FIG. 4 depicts a flowchart illustrating a statistical matching modulefunctionality during the deployment of an enhanced chatflow system,according to embodiments described herein. Upon reception of user input150, the enhanced chatflow system can transform the user input into auser input vector representation 400 using the same conversion methodused during the training mode. Alternately, the system can use analternate vector conversion method. The statistical matching module 125(as shown in FIG. 1) can use the classifiers 127 (as shown in FIG. 1)trained during the training mode as the basis for the statisticalanalysis. The vectored input can be processed by the statisticalmatching module 125 against the trained classifier to calculate theprobability that the particular user input 150 belongs to one of thecorresponding classes, which can be a dialog node 401. Depending on thetraining of the classifier, the system can also calculate theprobability that the particular user input 150 belongs to a directtrigger for a response or action. The probability can be used as aconfidence score 402, which can be used by the system to choose theclass to which the user input 150 has the highest likelihood ofcorresponding, or which can be used for a subsequent fusion methodology.

FIG. 5 depicts a flowchart illustrating hybrid rule-based andstatistical matching functionality during the deployment of an enhancedchatflow system, according to embodiments described herein. The enhancedchatflow system, upon receiving user input 150, can first subject theuser input to analysis by a standard rule-based module 126. Therule-based module can perform regular expression matching and/or exacttext matching 500 to determine if the user input 150 corresponds to aknown dialog node. If the regular expression matching and/or exact textmatching is successful, the system can process the user input 150 inaccordance with the matched dialog node 501. If the rule-based module126 fails to match the user input 150 with a known dialog node, thesystem can process the user input 150 using a statistical matchingmodule 125, which can create a vector representation of the user input502, and then subject the vector representation to a method ofstatistical matching using the classifiers previously trained (asdescribed in FIG. 4) in order to determine the probability that the userinput belongs to a particular dialog node 503. Using the calculatedprobabilities, the system can process the user input 150 in accordancewith the chosen dialog node 504. In an embodiment, the chosen dialognode 504 can be the dialog node having the highest probability ofcorresponding with the particular user input 150 if the probability ishigher than a defined threshold.

FIG. 6 depicts a flowchart illustrating hybrid rule-based andstatistical matching functionality during the deployment of an enhancedchatflow system, according to embodiments described herein. In anembodiment, the enhanced chatflow system, upon receiving user input 150,can simultaneously subject the user input 150 to analysis by arule-based module 126 and a statistical matching module 125. The rulebased module can perform regular expression matching or exact textmatching 600 to determine if the user input 150 corresponds to a knownmatching dialog node, and can return a known dialog node confidencescore 601 corresponding to the likelihood the user input matches with aknown node. Concurrently, the system can process the user input 150using a statistical matching module 125, which can create a vectorrepresentation of the user input 602, and then subject the vectorrepresentation to a method of statistical matching using the classifierspreviously trained (as described in FIG. 4) in order to determine theprobability that the user input belongs to a particular dialog node, andcan output a matching dialog node confidence score 603. The system canthen perform a fusion analysis 604 in order to determine the chosendialog node 605 to which the user input corresponds.

Fusion analysis 604 methods to choose the results can be many and theireffectiveness can depend on the task. In an embodiment, both thestatistical matching module 125 and the rule-based module 126 can returna confidence score regarding the top result or the k-top results.Methods of fusion analysis can include, but are not limited to: bestscore, weighted score, recall weighted score, and best-worst weightedscore. The weights for fusing the results can be trained using examplesfrom the training set, which can be the input variations.

Referring back to FIG. 1, in order to continuously update the enhancedchatflow system 120, several methodologies can be applied:

In an embodiment, expert-designed variations 130 can be used both forthe rule-based module 126 and the statistical matching module 125.Further expansion of the regular expressions and delimiters withsynonyms, example sentences, etc. can be either hand crafted orharvested through external data sources, like the corpus 140 or theinternet in order to increase the amount of data to train thestatistical matching module 125.

In an embodiment, expert-designed variations 130 can be used solely forthe rule-based module 126. Usage logs 131 can be used to extend therule-based module's 126 nodes and variations. The statistical matchingmodule 125 can use both the variations 130 and the usage logs 131 tocontinuously train.

In an embodiment, expert-designed variations 130 can be used solely forthe rule-based module 126, which can be subsequently fixed oncedeployed. Fixation can result in a pre-set, unalterable set of rules andmatching expressions for use in matching the user input 150 to acorresponding dialog node. The statistical matching module 125 can useboth the variations 130 and the usage logs 131 to continuously train,and can include the introduction of new classes.

In an embodiment, expert-designed variations 130 can be used solely forthe rule-based module 126, which can be fixed once deployed. Thestatistical matching module 125 can be trained only on the usage logs131 to catch cases where the rule-based module 126 fails.

In an embodiment, expert-designed variations 130 can be used for therule-based module 126 for the initially deployed system. The statisticalmatching module 125 can be trained on variations 130 and usage logs 131and can gradually replace the rule-based module 126.

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of,” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples are intendedto be non-limiting and are not exhaustive of the various possibilitiesfor implementing the mechanisms of the illustrative embodiments. It willbe apparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the example provided herein without departing from thespirit and scope of the present invention.

The system and processes of the figures are not exclusive. Othersystems, processes and menus may be derived in accordance with theprinciples of embodiments described herein to accomplish the sameobjectives. It is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the embodiments. Asdescribed herein, the various systems, subsystems, agents, managers, andprocesses can be implemented using hardware components, softwarecomponents, and/or combinations thereof. No claim element herein is tobe construed under the provisions of 35 U.S.C. 112, sixth paragraph,unless the element is expressly recited using the phrase “means for.”

Although the invention has been described with reference to exemplaryembodiments, it is not limited thereto. Those skilled in the art willappreciate that numerous changes and modifications may be made to thepreferred embodiments of the invention and that such changes andmodifications may be made without departing from the true spirit of theinvention. It is therefore intended that the appended claims beconstrued to cover all such equivalent variations as fall within thetrue spirit and scope of the invention.

What is claimed is:
 1. A computer implemented method, in a dataprocessing system comprising a processor and a memory comprisinginstructions which are executed by the processor to cause the processorto train an enhanced chatflow system, the method comprising: ingesting,using a rule-based module, a corpus of information comprising at leastone user input node corresponding to a user question and at least oneexpert-designed variation for each user input node; matching, using therule-based module, one or more user inputs to one or more correspondingdialog nodes using regular expressions and delimiters; ingesting, usinga statistical matching module, one or more usage logs from a deployeddialog system, each usage log comprising at least one user input node;for each user input node: designating the node as a class; storing thenode in a dialog node repository; designating each of the at least onevariations as training examples for the designated class; converting theclasses and the training examples into fixed-length feature vectorrepresentations, wherein variable length sentences are transformed tothe fixed-length feature vector representations using convolutionalneural networks; training one or more classifiers using the fixed-lengthfeature vector representations of the classes; wherein the step oftraining the one or more classifiers further comprising: fixing therule-based module such that no further alteration to the regularexpressions or delimiters is allowed; ingesting, using the statisticalmatching module, the expert-designed variations in addition to the usagelogs; and training, using the statistical matching module, the one ormore classifiers based upon analysis of the expert-designed variationsand the usage logs; training classification objectives using thefixed-length feature vector representations of the training examples;and incorporating the training of the classifiers and the classificationobjectives into the enhanced chatflow system.
 2. The method as recitedin claim 1, further comprising: ingesting, using the statisticalmatching module, the expert-designed variations in addition to the usagelogs.
 3. The method as recited in claim 1, further comprising:expanding, using the rule-based module, the regular expressions anddelimiters using synonyms harvested through one or more external datasources.
 4. The method as recited in claim 1, further comprising:ingesting, using the statistical matching module, the expert-designedvariations in addition to the usage logs; training, using thestatistical matching module, new classes based upon analysis of theexpert-designed variations and the usage logs; and replacing therule-based module with the statistical matching module.
 5. The method asrecited in claim 1, further comprising: training, using the statisticalmatching module, the classifiers and the classification objectives usingDeep Belief Network (DBN) classifiers.
 6. A computer program product forenhanced chatflow, the computer program product comprising a computerreadable storage medium having program instructions embodied therewith,the program instructions executable by a processor to cause theprocessor to: ingest, using a rule-based module, a corpus of informationcomprising at least one user input node corresponding to a user questionand at least one expert-designed variation for each user input node;match, using the rule-based module, one or more user inputs to one ormore corresponding dialog nodes using regular expressions anddelimiters; ingest, using a statistical matching module, one or moreusage logs from a deployed dialog system, each usage log comprising atleast one user input node; for each user input node: designate the nodeas a class; store the node in a dialog node repository; designate eachof the at least one variations as training examples for the designatedclass; convert the classes and the training examples into fixed-lengthfeature vector representations, wherein variable length sentences aretransformed to the fixed-length feature vector representations usingconvolutional neural networks; train one or more classifiers using thefixed-length feature vector representations of the classes; wherein thestep of training the one or more classifiers further comprising: fix therule-based module such that no further alteration to the regularexpressions or delimiters is allowed; ingest, using the statisticalmatching module, the expert-designed variations in addition to the usagelogs; and train, using the statistical matching module, the one or moreclassifiers based upon analysis of the expert-designed variations andthe usage logs; train classification objectives using the fixed-lengthfeature vector representations of the training examples; and incorporatethe training of the classifiers and the classification objectives intothe enhanced chatflow system.
 7. The computer program product as recitedin claim 6, wherein the processor is further caused to: ingest, usingthe statistical matching module, the expert-designed variations inaddition to the usage logs.
 8. The computer program product as recitedin claim 6, wherein the processor is further caused to: expand, usingthe rule-based module, the regular expressions and delimiters usingsynonyms harvested through one or more external data sources.
 9. Thecomputer program product as recited in claim 6, wherein the processor isfurther caused to: ingest, using the statistical matching module, theexpert-designed variations in addition to the usage logs; train, usingthe statistical matching module, new classes based upon analysis of theexpert-designed variations and the usage logs; and replace therule-based module with the statistical matching module.
 10. The computerprogram product as recited in claim 6, wherein the processor is furthercaused to: train, using the statistical matching module, the classifiersand the classification objectives using Deep Belief Network (DBN)classifiers.
 11. An enhanced chatflow system, comprising: A processorand a memory comprising instructions which are executed by the processorto cause the processor to: ingest, using a rule-based module, a corpusof information comprising at least one user input node corresponding toa user question and at least one expert-designed variation for each userinput node; match, using the rule-based module, one or more user inputsto one or more corresponding dialog nodes using regular expressions anddelimiters; ingest, using a statistical matching module, one or moreusage logs from a deployed dialog system, each usage log comprising atleast one user input node; for each user input node: designate the nodeas a class; store the node in a dialog node repository; designate eachof the at least one variations as training examples for the designatedclass; convert the classes and the training examples into fixed-lengthfeature vector representations, wherein variable length sentences aretransformed to the fixed-length feature vector representations usingconvolutional neural networks; train one or more classifiers using thefixed-length feature vector representations of the classes; wherein thestep of training the one or more classifiers further comprising: fix therule-based module such that no further alteration to the regularexpressions or delimiters is allowed; ingest, using the statisticalmatching module, the expert-designed variations in addition to the usagelogs; and train, using the statistical matching module, the one or moreclassifiers based upon analysis of the expert-designed variations andthe usage logs; train classification objectives using the fixed-lengthfeature vector representations of the training examples; and incorporatethe training of the classifiers and the classification objectives intothe enhanced chatflow system.
 12. The system as recited in claim 11,wherein the enhanced chatflow processor is further configured to:ingest, using the statistical matching module, the expert-designedvariations in addition to the usage logs.
 13. The system as recited inclaim 11, wherein the enhanced chatflow processor is further configuredto: expand, using the rule-based module, the regular expressions anddelimiters using synonyms harvested through one or more external datasources.
 14. The system as recited in claim 11, wherein the enhancedchatflow processor is further configured to: ingest, using thestatistical matching module, the expert-designed variations in additionto the usage logs; train, using the statistical matching module, newclasses based upon analysis of the expert-designed variations and theusage logs; and replace the rule-based module with the statisticalmatching module.
 15. The system as recited in claim 11, wherein theenhanced chatflow processor is further configured to: train, using thestatistical matching module, the classifiers and the classificationobjectives using Deep Belief Network (DBN) classifiers.